Summary: Given a series of multivariate data, approximate it by a piece-wise constant function. How many cut-points are there? What are the means and variances of the segments? Where should the cut points be placed? The simplest model is a single segment. The most complex model has one segment per data point. The best model is generally somewhere between these extremes. Only by considering model complexity can a reasonable inference be made.
Also see [slides] & [PRICAI 2002].